using chc 6
present CV LDAs, have testing/training if need be
graphed LDAs are all the points.

MANOVA for species level

fit_full_species_man$pca_summary 
## Importance of first k=7 (out of 35) components:
##                           PC1    PC2     PC3     PC4     PC5     PC6     PC7
## Standard deviation     1.0413 0.7144 0.51001 0.38099 0.34918 0.30663 0.28705
## Proportion of Variance 0.3871 0.1822 0.09287 0.05183 0.04354 0.03357 0.02942
## Cumulative Proportion  0.3871 0.5694 0.66225 0.71408 0.75762 0.79119 0.82061
summary(manova(as.matrix(data[,4:cols]) ~ hostRace * sex *site, data = data), test = "Wilks")
##                    Df   Wilks approx F num Df den Df    Pr(>F)    
## hostRace            4 0.04507   53.399     28 1097.5 < 2.2e-16 ***
## sex                 1 0.71370   17.421      7  304.0 < 2.2e-16 ***
## site                8 0.26950    8.085     56 1642.4 < 2.2e-16 ***
## hostRace:sex        4 0.80887    2.375     28 1097.5 8.147e-05 ***
## hostRace:site       1 0.96866    1.405      7  304.0  0.202640    
## sex:site            7 0.77071    1.663     49 1547.8  0.002979 ** 
## hostRace:sex:site   1 0.97168    1.266      7  304.0  0.266990    
## Residuals         310                                             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

PCA for Species Level

LDA for Species level

##            Reference
## Prediction  Cingulata Cornivora Mendax pom Zepheria
##   Cingulata        29         0      0   0        0
##   Cornivora         1         5      0   1        0
##   Mendax            0         0     60   8        2
##   pom               0         0      1 214        0
##   Zepheria          0         0      0   1       15
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   9.584570e-01   9.213168e-01   9.312796e-01   9.771049e-01   6.646884e-01 
## AccuracyPValue  McnemarPValue 
##   2.679678e-40            NaN

Manova for pomonella

summary(manova(as.matrix(data[,4:cols]) ~ hostRace * site * sex, data = data), test = "Wilks")
##                    Df   Wilks approx F num Df den Df    Pr(>F)    
## hostRace            1 0.70245  12.2841      7 203.00 4.504e-13 ***
## site                4 0.37641   8.1524     28 733.35 < 2.2e-16 ***
## sex                 1 0.48494  30.8012      7 203.00 < 2.2e-16 ***
## hostRace:site       2 0.72182   5.1338     14 406.00 6.340e-09 ***
## hostRace:sex        1 0.95257   1.4440      7 203.00  0.189418    
## site:sex            4 0.77940   1.8745     28 733.35  0.004284 ** 
## hostRace:site:sex   1 0.92648   2.3014      7 203.00  0.028089 *  
## Residuals         209                                             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

LDA for Pomonella

##           Reference
## Prediction Apple Haw
##      Apple    80  22
##      Haw      26  96
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.857143e-01   5.693688e-01   7.261263e-01   8.375728e-01   5.267857e-01 
## AccuracyPValue  McnemarPValue 
##   8.355647e-16   6.650055e-01

LDA predicting host and sex for pomonella

##               Reference
## Prediction     Apple_Female Apple_Male Haw_Female Haw_Male
##   Apple_Female           51          5         17        2
##   Apple_Male              1         21          1        8
##   Haw_Female             19          1         44        5
##   Haw_Male                0          8          4       37
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.830357e-01   5.662230e-01   6.177391e-01   7.434095e-01   3.169643e-01 
## AccuracyPValue  McnemarPValue 
##   3.310270e-29   5.581411e-01

MANOVA for Zepheria

# no interaction because missing males at MtPleasant
summary(manova(as.matrix(data[,4:cols]) ~ sex + site, data = data), test = "Wilks")
##           Df   Wilks approx F num Df den Df Pr(>F)
## sex        1 0.74708  0.67708      5     10 0.6507
## site       1 0.83749  0.38808      5     10 0.8461
## Residuals 14

LDA for Zepheria

##                  Reference
## Prediction        Zepheria_Female Zepheria_Male
##   Zepheria_Female               5             3
##   Zepheria_Male                 4             5
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5882353      0.1793103      0.3292472      0.8155630      0.5294118 
## AccuracyPValue  McnemarPValue 
##      0.4062810      1.0000000

LDA for sex +site zepheria

##                              Reference
## Prediction                    Zepheria_Female_EastLansing
##   Zepheria_Female_EastLansing                           2
##   Zepheria_Female_MtPleasant                            0
##   Zepheria_Male_EastLansing                             4
##                              Reference
## Prediction                    Zepheria_Female_MtPleasant
##   Zepheria_Female_EastLansing                          2
##   Zepheria_Female_MtPleasant                           1
##   Zepheria_Male_EastLansing                            0
##                              Reference
## Prediction                    Zepheria_Male_EastLansing
##   Zepheria_Female_EastLansing                         1
##   Zepheria_Female_MtPleasant                          5
##   Zepheria_Male_EastLansing                           2
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.29411765    -0.05699482     0.10313551     0.55958272     0.47058824 
## AccuracyPValue  McnemarPValue 
##     0.95792652     0.03207164

MANOVA for Mendax

summary(manova(as.matrix(data[,4:cols]) ~ sex * site, data = data), test = "Wilks")
##           Df   Wilks approx F num Df den Df    Pr(>F)    
## sex        1 0.57727   6.1024      6     50 7.541e-05 ***
## site       2 0.10716  17.1232     12    100 < 2.2e-16 ***
## sex:site   2 0.74705   1.3082     12    100    0.2258    
## Residuals 55                                             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

LDA for Mendax

##                Reference
## Prediction      Mendax_Female Mendax_Male
##   Mendax_Female            20          15
##   Mendax_Male              13          13
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.54098361     0.07072905     0.40849889     0.66935590     0.54098361 
## AccuracyPValue  McnemarPValue 
##     0.55241652     0.85010674

LDA for sex +site mendax

##                         Reference
## Prediction               Mendax_Female_Fenville Mendax_Female_OtisLake
##   Mendax_Female_Fenville                      7                      1
##   Mendax_Female_OtisLake                      1                      5
##   Mendax_Female_Sewanee                       0                      2
##   Mendax_Male_Fenville                        6                      0
##   Mendax_Male_OtisLake                        0                      1
##   Mendax_Male_Sewanee                         0                      0
##                         Reference
## Prediction               Mendax_Female_Sewanee Mendax_Male_Fenville
##   Mendax_Female_Fenville                     0                    5
##   Mendax_Female_OtisLake                     1                    0
##   Mendax_Female_Sewanee                      4                    1
##   Mendax_Male_Fenville                       0                    5
##   Mendax_Male_OtisLake                       1                    1
##   Mendax_Male_Sewanee                        4                    0
##                         Reference
## Prediction               Mendax_Male_OtisLake Mendax_Male_Sewanee
##   Mendax_Female_Fenville                    0                   0
##   Mendax_Female_OtisLake                    1                   2
##   Mendax_Female_Sewanee                     2                   3
##   Mendax_Male_Fenville                      0                   1
##   Mendax_Male_OtisLake                      1                   1
##   Mendax_Male_Sewanee                       2                   3
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##    0.409836066    0.283523654    0.285504382    0.543223627    0.229508197 
## AccuracyPValue  McnemarPValue 
##    0.001280557            NaN

MANOVA for cingulata

summary(manova(as.matrix(data[,4:cols]) ~ sex * site, data = data), test = "Wilks")
##           Df   Wilks approx F num Df den Df   Pr(>F)   
## sex        1 0.80383   1.2813      4     21 0.308955   
## site       2 0.39036   3.1529      8     42 0.006927 **
## sex:site   2 0.66131   1.2059      8     42 0.319040   
## Residuals 24                                           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

LDA for Cingulata

##                   Reference
## Prediction         Cingulata_Female Cingulata_Male
##   Cingulata_Female                8              6
##   Cingulata_Male                  7              9
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5666667      0.1333333      0.3742735      0.7453925      0.5000000 
## AccuracyPValue  McnemarPValue 
##      0.2923324      1.0000000

LDA for sex +site cingulata

##                             Reference
## Prediction                   Cingulata_Female_Fenville
##   Cingulata_Female_Fenville                          1
##   Cingulata_Female_SouthBend                         0
##   Cingulata_Female_Urbana                            0
##   Cingulata_Male_Fenville                            1
##   Cingulata_Male_SouthBend                           0
##   Cingulata_Male_Urbana                              1
##                             Reference
## Prediction                   Cingulata_Female_SouthBend Cingulata_Female_Urbana
##   Cingulata_Female_Fenville                           3                       0
##   Cingulata_Female_SouthBend                          2                       0
##   Cingulata_Female_Urbana                             4                       1
##   Cingulata_Male_Fenville                             0                       0
##   Cingulata_Male_SouthBend                            0                       0
##   Cingulata_Male_Urbana                               1                       1
##                             Reference
## Prediction                   Cingulata_Male_Fenville Cingulata_Male_SouthBend
##   Cingulata_Female_Fenville                        0                        4
##   Cingulata_Female_SouthBend                       0                        1
##   Cingulata_Female_Urbana                          0                        1
##   Cingulata_Male_Fenville                          0                        1
##   Cingulata_Male_SouthBend                         0                        1
##   Cingulata_Male_Urbana                            0                        2
##                             Reference
## Prediction                   Cingulata_Male_Urbana
##   Cingulata_Female_Fenville                      0
##   Cingulata_Female_SouthBend                     2
##   Cingulata_Female_Urbana                        0
##   Cingulata_Male_Fenville                        2
##   Cingulata_Male_SouthBend                       0
##   Cingulata_Male_Urbana                          0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.17241379     0.04000000     0.05845608     0.35774755     0.34482759 
## AccuracyPValue  McnemarPValue 
##     0.98826571            NaN

MANOVA for Cornivora

There is only one site

# no site because only sampled at one site.
summary(manova(as.matrix(data[,4:cols]) ~ sex, data = data), test = "Wilks")
##           Df   Wilks approx F num Df den Df Pr(>F)
## sex        1 0.54944  0.82004      2      2 0.5494
## Residuals  3

LDA for cornivora

##                   Reference
## Prediction         Cornivora_Female Cornivora_Male
##   Cornivora_Female                0              2
##   Cornivora_Male                  0              2
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.50000000     0.00000000     0.06758599     0.93241401     1.00000000 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.47950012

lines of diversification

euclidian distance

##                  Apple_Female Apple_Male Cingulata_Female Cingulata_Male
## Apple_Female        0.0000000                                           
## Apple_Male          1.1052241  0.0000000                                
## Cingulata_Female    1.4738114  1.6036342        0.0000000               
## Cingulata_Male      1.4051571  1.6128575        0.1130547      0.0000000
## Cornivora_Female    1.8164505  1.6514454        0.4825512      0.5956054
## Cornivora_Male      2.3568964  1.7473686        1.3312107      1.4407462
## Haw_Female          0.5292825  1.5234184        1.9508336      1.8698619
## Haw_Male            0.6482415  0.5290546        1.6459586      1.6184269
## Mendax_Female       1.1742028  2.0634150        1.2374012      1.1243501
## Mendax_Male         1.0269548  1.8389388        1.0200228      0.9078379
## Zepheria_Female     2.2810030  3.3164335        2.4854319      2.3757562
## Zepheria_Male       2.3942415  3.4468577        2.6586999      2.5484713
##                  Cornivora_Female Cornivora_Male Haw_Female  Haw_Male
## Apple_Female                                                         
## Apple_Male                                                           
## Cingulata_Female                                                     
## Cingulata_Male                                                       
## Cornivora_Female        0.0000000                                    
## Cornivora_Male          0.8731903      0.0000000                     
## Haw_Female              2.3257992      2.8860880  0.0000000          
## Haw_Male                1.8371458      2.1364704  1.0022950 0.0000000
## Mendax_Female           1.7199319      2.5456134  1.3364646 1.7570533
## Mendax_Male             1.5002006      2.3102051  1.2814072 1.5661386
## Zepheria_Female         2.9567427      3.8164525  2.1620829 2.9257376
## Zepheria_Male           3.1317505      3.9891585  2.2408291 3.0416391
##                  Mendax_Female Mendax_Male Zepheria_Female Zepheria_Male
## Apple_Female                                                            
## Apple_Male                                                              
## Cingulata_Female                                                        
## Cingulata_Male                                                          
## Cornivora_Female                                                        
## Cornivora_Male                                                          
## Haw_Female                                                              
## Haw_Male                                                                
## Mendax_Female        0.0000000                                          
## Mendax_Male          0.2497539   0.0000000                              
## Zepheria_Female      1.3207469   1.5704754       0.0000000              
## Zepheria_Male        1.4794064   1.7289277       0.1809107     0.0000000

mds plot

centroids

site centroids